Update README.md

README.md

@@ -94,17 +94,17 @@ The resulting embedding is stored as a JSON stringified array of 1024 floating p
 
 ## 🎓 Tutorials
 
-### 1. 🔄 The chunking doesn't fit your use case?
+### 🔄 1. The chunking doesn't fit your use case?
 
 If you need to reconstitute the original, un-chunked dataset, you can follow [this tutorial notebook available on our GitHub repository](https://github.com/etalab-ia/mediatech/blob/main/docs/reconstruct_vector_database.ipynb).
 
 ⚠️ The tutorial is only relevant for datasets that were chunked **without overlap**.
 
-### 2. 🤖 How to load MediaTech's datasets from Hugging Face and use them in a RAG pipeline ?
+### 🤖 2. How to load MediaTech's datasets from Hugging Face and use them in a RAG pipeline ?
 
 To learn how to load MediaTech's datasets from Hugging Face and integrate them into a Retrieval-Augmented Generation (RAG) pipeline, check out our [step-by-step RAG tutorial available on our GitHub repository !](https://github.com/etalab-ia/mediatech/blob/main/docs/hugging_face_rag_tutorial.ipynb)
 
-### 3. 📌 Embedding Use Notice
+### 📌 3. Embedding Use Notice
 
 ⚠️ The `embeddings_bge-m3` column is stored as a **stringified list** of floats (e.g., `"[-0.03062629,-0.017049594,...]"`). 
 To use it as a vector, you need to parse it into a list of floats or NumPy array.